Evaluating AI Tools in Community Participation for Industrial Heritage Regeneration in Nanchang

Enhancing community participation in industrial heritage regeneration is necessary for sustainable city improvement. This research study explore how artificial intelligence (AI) tools can empower community involvement, using a case study in the Qingshan Lake District of Nanchang, China. We develop a theoretical model that includes the Technology Acceptance Model (TAM) and Expectation Confirmation Theory (ECT) to link AI applications with community participation outcomes. A research study of 430 local residents and stakeholders was performed through convenience sampling, and the data were analyzed using Structural Equation Modeling (SEM). The results reveal that perceived ease of use (PEU), perceived usefulness (PU), and expectation confirmation (EC) favorably effect community participation satisfaction (SAT). In turn, greater satisfaction causes greater participation (APB) and improved heritage regeneration outcomes (RE), with overall satisfaction mediating the relationship between AI factors and outcomes. These findings extend the TAM and ECT structures to the industrial heritage context and reveal the mechanisms of AI-enabled participation. Practically, the study provides insights for governments and organizations to take advantage of AI in enhancing public engagement. Nonetheless, the research study is limited by its single-case design and non-random sample. Future future studies should examine more comprehensive contexts and incorporate additional socio-economic and technological elements to verify the generalizability of the findings.